4.3 KiB
Sharing pipelines and models
Share your pipelines, models, and schedulers on the Hub with [~utils.PushToHubMixin]. This mixin:
- creates a repository on the Hub
- saves your model, scheduler, or pipeline files so they can be reloaded later
- uploads the folder containing these files to the Hub
Log in to your Hugging Face account with your access token.
from huggingface_hub import notebook_login
notebook_login()
hf auth login
Push to your user namespace with a short id ("my-controlnet-model") or to an org with "your-org/my-controlnet-model".
Models
To push a model to the Hub, call [~utils.PushToHubMixin.push_to_hub] and specify the repository id of the model.
from diffusers import ControlNetModel
controlnet = ControlNetModel(
block_out_channels=(32, 64),
layers_per_block=2,
in_channels=4,
down_block_types=("DownBlock2D", "CrossAttnDownBlock2D"),
cross_attention_dim=32,
conditioning_embedding_out_channels=(16, 32),
)
controlnet.push_to_hub("my-controlnet-model")
The [~utils.PushToHubMixin.push_to_hub] method saves the model's config.json file and the weights are automatically saved as safetensors files.
Load the model again with [ControlNetModel.from_pretrained].
model = ControlNetModel.from_pretrained("your-namespace/my-controlnet-model")
Scheduler
To push a scheduler to the Hub, call [~utils.PushToHubMixin.push_to_hub] and specify the repository id of the scheduler.
from diffusers import DDIMScheduler
scheduler = DDIMScheduler(
beta_start=0.00085,
beta_end=0.012,
beta_schedule="scaled_linear",
clip_sample=False,
set_alpha_to_one=False,
)
scheduler.push_to_hub("my-ddim-scheduler")
The [~utils.PushToHubMixin.push_to_hub] method saves the scheduler's scheduler_config.json file to the specified repository.
Load the scheduler again with [~SchedulerMixin.from_pretrained].
scheduler = DDIMScheduler.from_pretrained("your-namespace/my-ddim-scheduler")
Pipeline
To push a pipeline to the Hub, load it with [~DiffusionPipeline.from_pretrained], then call [~utils.PushToHubMixin.push_to_hub] with a repository id.
import torch
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained(
"Qwen/Qwen-Image", dtype=torch.bfloat16, device_map="cuda" # or "mps", "xpu", "cpu"
)
pipeline.push_to_hub("your-namespace/my-qwen-image")
The [~utils.PushToHubMixin.push_to_hub] method saves each component to a subfolder in the repository. Load the pipeline again with [DiffusionPipeline.from_pretrained].
pipeline = DiffusionPipeline.from_pretrained("your-namespace/my-qwen-image")
Privacy
Set private=True in [~utils.PushToHubMixin.push_to_hub] to keep a model, scheduler, or pipeline files private.
controlnet.push_to_hub("my-controlnet-model-private", private=True)
Pass create_pr=True to open a pull request on an existing Hub repository instead of pushing straight to the default branch.
Models and pipelines also accept variant= on push when you want a named weight file such as fp16. Schedulers do not use variant.
Private repositories are only visible to you. Other users won't be able to clone the repository and it won't appear in search results. Even if a user has the URL to your private repository, they'll receive a 404 - Sorry, we can't find the page you are looking for. You must be logged in to load a model from a private repository.